Lightweight DETR for Small-Target Defect Detection in Stators and Rotors of Pumped Storage Generators

Abstract

Real-time detection of micro-defects in pumped storage generator stators and rotors remains challenging due to small-target obscurity and edge deployment constraints. This paper proposes EdgeFault-detection transformer (DETR), a lightweight transformer model that integrates three innovations: (1) dynamic geometric-photometric augmentation for robustness, (2) a FasterNet backbone with Partial Convolution (PConv) that reduces the number of parameters by 40% (from 20 × 10⁶ to 12 × 10⁶), and (3) cross-scale small-object head enhancing defect localisation. Experiments on 8763 industrial images demonstrate 75.38% [email protected] (+17.25% over RT-DETR) and 49.3% mAP_small (+42.9% from baseline). The model achieves 22 FPS on NVIDIA RTX A4000 GPUs (640 × 640 resolution), validating real-time industrial applicability. Strategic computation allocation increases GFLOPs (giga floating-point operations) by 16.4% (from 58.6 to 68.2) to prioritise safety–critical precision, justifying the trade-off for detecting high-risk anomalies (e.g., insulation cracks).